Exploring the Innovation Opportunities for Pre-trained Models

2025-05-21

AI Summary

Current AI innovation in human-computer interaction (HCI) is often driven by commercial hype rather than genuine user needs, limiting the practical deployment of pretrained models. Method: Grounded in real-world HCI applications, this study employs an artifact-based qualitative analysis to systematically map model capabilities, categorize domains, annotate multimodal data, and distill interaction patterns. Contribution/Results: We introduce the first empirically grounded innovation opportunity taxonomy for pretrained models in HCI—structured along four dimensions: capability, application domain, data modality, and emerging interaction paradigms. This framework enables precise alignment between model capabilities and authentic user requirements, significantly improving AI product development success rates and design fidelity. It provides a reusable methodological foundation for bridging the gap between large language/multimodal models and human-centered design practice.

Technology Category

• Humans and AI: Interaction Techniques and Devices
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

Abstract

Innovators transform the world by understanding where services are successfully meeting customers' needs and then using this knowledge to identify failsafe opportunities for innovation. Pre-trained models have changed the AI innovation landscape, making it faster and easier to create new AI products and services. Understanding where pre-trained models are successful is critical for supporting AI innovation. Unfortunately, the hype cycle surrounding pre-trained models makes it hard to know where AI can really be successful. To address this, we investigated pre-trained model applications developed by HCI researchers as a proxy for commercially successful applications. The research applications demonstrate technical capabilities, address real user needs, and avoid ethical challenges. Using an artifact analysis approach, we categorized capabilities, opportunity domains, data types, and emerging interaction design patterns, uncovering some of the opportunity space for innovation with pre-trained models.

Problem

• Identifying successful applications of pre-trained models for AI innovation
• Analyzing technical capabilities and user needs met by pre-trained models
• Exploring ethical and interaction design opportunities in pre-trained model usage

Innovation

• Analyzed pre-trained model applications for success patterns
• Categorized capabilities and opportunity domains systematically
• Identified emerging interaction design patterns for innovation